Power augmentation of ducted wind turbines for urban structures: Experimental, numerical, and economic approaches
Bibliographic record
Abstract
Abstract Recent development in using wind turbines for urban areas results in inserting turbines inside buildings. As buildings' walls may act as a duct for the turbine, this study focuses on a ducted wind turbine with a fixed duct geometry. A method is organized for achieving the improved generated power and the wind speed augmentation with fixed geometry of duct regardless of the type of the turbine, which is the aim of building designers. Using a porous disc (PD) instead of a wind turbine rotor makes the study cost and time effective. PDs within a duct help estimate any given duct's maximum available power extraction capability. In addition, experimental and numerical tests examine the effect of PDs solidity on the performance of diffuser augmented wind turbines and the corresponding economic analysis. Both experimental and numerical results agree that the power coefficient highly depends on the solidities of the PD. The power coefficient of a ducted PD with a solidity of 0.3 is augmented by up to 30%. Nevertheless, in some cases, employing a duct can contribute to the power reduction if the solidity exceeds a critical value. A smoke visualization technique helps vortex study. Economic assessment of a ducted turbine for three scenarios belonging to Germany and Italy shows a 15.3% decline in cost per electricity production. The payback period decreases by 3.42 years, 7.68 months, and 6.36 months for Scenarios 1, 2, and 3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".